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Deep learning methods in medical image-based hepatocellular carcinoma diagnosis: a systematic review and meta-analysis
Q Wei, N Tan, S **ong, W Luo, H **a, B Luo - Cancers, 2023 - mdpi.com
Simple Summary In this study, after conducting a comprehensive review of 1356 papers that
evaluated the diagnostic performance of deep learning (DL) methods based on medical …
evaluated the diagnostic performance of deep learning (DL) methods based on medical …
Artificial intelligence techniques in liver cancer
Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant
contributor to worldwide cancer-related deaths. Various medical imaging techniques …
contributor to worldwide cancer-related deaths. Various medical imaging techniques …
En–Denet based segmentation and gradational modular network classification for liver cancer diagnosis
Liver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed
tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides …
tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides …
Recent advances in deep learning and medical imaging for cancer treatment
In the evolving landscape of medical imaging, the escalating need for deep-learning
methods takes center stage, offering the capability to autonomously acquire abstract data …
methods takes center stage, offering the capability to autonomously acquire abstract data …
Usefulness of T2-weighted images with deep-learning-based reconstruction in nasal cartilage
Y Gao, W Liu, L Li, C Liu, Y Zha - Diagnostics, 2023 - mdpi.com
Objective: This study aims to evaluate the feasibility of visualizing nasal cartilage using deep-
learning-based reconstruction (DLR) fast spin-echo (FSE) imaging in comparison to three …
learning-based reconstruction (DLR) fast spin-echo (FSE) imaging in comparison to three …
A Review on Medical Image Segmentation: Datasets, Technical Models, Challenges and Solutions
Medical image segmentation is prerequisite in computer‐aided diagnosis. As the field
experiences tremendous paradigm changes since the introduction of foundation models …
experiences tremendous paradigm changes since the introduction of foundation models …
A Systematic Review on Medical Image Segmentation using Deep Learning
Medical image segmentation is an essential step in various diagnostic and treatment
procedures. This study aimed to conduct a systematic review of state-of-the-art segmentation …
procedures. This study aimed to conduct a systematic review of state-of-the-art segmentation …
ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes
Semantic segmentation of volumetric medical images is essential for accurate delineation of
anatomic structures and pathology, enabling quantitative analysis in precision medicine …
anatomic structures and pathology, enabling quantitative analysis in precision medicine …
Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification
Diagnosing liver disease presents a significant medical challenge in impoverished
countries, with over 30 billion individuals succumbing to it each year. Existing models for …
countries, with over 30 billion individuals succumbing to it each year. Existing models for …
DS-SwinUNet: Redesigning Skip Connection with Double Scale Attention for Land Cover Semantic Segmentation
In recent years, the development of visual transformer has gradually replaced convolutional
neural networks in the visual domain with attention computation, causing pure transformer …
neural networks in the visual domain with attention computation, causing pure transformer …